Purpose

The purpose of this study is to compare interpolation algorithms and deep neural networks for inverse transfer problems with linear and nonlinear behaviour.

Design/methodology/approach

A series of runs were conducted for a canonical test problem. These were used as databases or “learning sets” for both interpolation algorithms and deep neural networks. A second set of runs was conducted to test the prediction accuracy of both approaches.

Findings

The results indicate that interpolation algorithms outperform deep neural networks in accuracy for linear heat conduction, while the reverse is true for nonlinear heat conduction problems. For heat convection problems, both methods offer similar levels of accuracy.

Originality/value

This is the first time such a comparison has been made.

中文翻译：

---

用于热和流体流动问题逆向建模的深度学习或插值？

目的

本研究的目的是针对具有线性和非线性行为的逆向转移问题比较插值算法和深度神经网络。

设计/方法/方法

针对典型测试问题进行了一系列运行。这些被用作插值算法和深度神经网络的数据库或“学习集”。进行第二组运行以测试两种方法的预测准确性。

发现

结果表明，插值算法在线性热传导的准确性上优于深度神经网络，而对于非线性热传导问题则相反。对于热对流问题，两种方法都提供相似的准确度。

原创性/价值

这是第一次进行这样的比较。